Gesture recognition using multiple antenna

ABSTRACT

Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.

RELATED APPLICATIONS

This application is a continuation application and claims priority toU.S. patent application Ser. No. 17/005,207 filed on Aug. 27, 2020 whichin turn claims priority to U.S. patent application Ser. No. 15/093,533filed on Apr. 7, 2016, which claims priority to U.S. Provisional PatentApplication Ser. No. 62/237,975 filed on Oct. 6, 2015, the disclosuresof which are incorporated by reference herein in their entireties.

BACKGROUND

This background description is provided for the purpose of generallypresenting the context of the disclosure. Unless otherwise indicatedherein, material described in this section is neither expressly norimpliedly admitted to be prior art to the present disclosure or theappended claims.

As computing devices evolve with more computing power, they are able toevolve how they receive input commands or information. One type ofevolving input mechanism relates to capturing user gestures. Forinstance, a user can attach a first peripheral device to their arm orhand that reads muscle activity, or hold a second peripheral device thatcontains an accelerometer that detects motion. In turn, theseperipherals then communicate with a receiving computing device basedupon a detected gesture. With these types of peripheral devices, a userphysically connects the peripheral device to a corresponding body partthat performs the gesture. However, this constrains the user, in thatthe user must not only acquire these peripheral devices, but must couplethem to the receiving computing device. Thus, it would be advantageousto capture various gestures without attaching a peripheral device to theuser.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter.

Various embodiments wirelessly detect micro gestures using multipleantennas of a gesture sensor device. At times, the gesture sensor devicetransmits multiple outgoing radio frequency (RF) signals, each outgoingRF signal transmitted via a respective antenna of the gesture sensordevice. The outgoing RF signals are configured to help captureinformation that can be used to identify micro-gestures performed by ahand. The gesture sensor device captures incoming RF signals generatedby the outgoing RF signals reflecting off of the hand, and then analyzesthe incoming RF signals to identify the micro-gesture.

One or more embodiments provide a device configured to identify amicro-gesture associated with a target object, the device comprising: atleast two antennas to respectively transmit a plurality of outgoingradio frequency (RF) signals, each antenna configured to: transmit arespective outgoing radio frequency (RF) signal of the plurality ofoutgoing RF signals; and receive an incoming RF signal generated by atleast one transmitted outgoing RF signal of the plurality of outgoing RFsignals reflecting off the target object; a digital signal processingcomponent configured to: process a first set of data originating fromincoming RF signals to extract information about the target object; anda machine-learning component configured to: receive the informationextracted by the digital signal processing component; and process theinformation to identify the micro-gesture.

At least one embodiment provides a method for identifying amicro-gesture performed by a hand, the method comprising: transmitting aplurality of outgoing RF signals, each outgoing RF signal transmitted ona respective antenna of a plurality of antennas; capturing at least twoincoming RF signals generated by at least two outgoing RF signals of theplurality of outgoing RF signals reflecting off the hand; and processingthe at least two captured incoming RF signals to identify themicro-gesture performed by the hand.

At least one embodiment provides a device for detecting a micro-gestureperformed by a hand, the device comprising: a gesture sensor componentcomprising: at least two antenna, each respective antenna associatedwith a respective transceiver path; at least one processor; one or morecomputer-readable storage devices; and one or more ApplicationProgramming Interfaces (APIs) stored on the one or morecomputer-readable storage devices which, responsive to execution by theat least one processor, configure the gesture sensor component to detectthe micro-gesture by causing the gesture sensor component to performoperations comprising: transmitting a plurality of outgoing radiofrequency (RF) signals, each outgoing radio frequency (RF) signal beingtransmitted on a respective antenna of the at least two antenna;receiving at least two incoming RF signals originating from at least twooutgoing RF signals of the plurality of outgoing RF signals reflectingoff the hand; and processing the at least two RF signals to detect themicro-gesture by processing data from at least two respectivetransceiver paths.

At least one embodiment provides a device configured to identify amicro-gesture associated with a target object, the device comprising:means for transmitting each respective outgoing radio frequency (RF)signal of a plurality of outgoing RF signals on a respective antenna;means for receiving incoming RF signals generated by at least some ofthe plurality of outgoing RF signals reflecting off the target object;means for processing a first set of data originating from incoming RFsignals to extract information about the target object; and means forprocessing the information to identify the micro-gesture.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of wireless micro-gesture detection are described withreference to the following drawings. The same numbers are usedthroughout the drawings to reference like features and components:

FIG. 1 illustrates an example environment that employs wirelessmicro-gesture detection in accordance with one or more embodiments;

FIG. 2 illustrates an example implementation of a computing device ofFIG. 1 in greater detail in accordance with one or more embodiments;

FIG. 3 illustrates an example of general signal properties;

FIG. 4 illustrates an example of general signal properties;

FIG. 5 illustrates an example of a pipeline in accordance with one ormore embodiments;

FIG. 6 illustrates an example flow diagram in accordance with one ormore embodiments;

FIG. 7 illustrates an example device in which micro-gesture handdetection can be employed in accordance with one or more embodiments.

DETAILED DESCRIPTION

Overview

Various embodiments wirelessly detect or recognize micro-gestures, e.g.,hand gestures, using multiple antennas of a gesture sensor device. Thesemicro-gestures can be detected in free-space, without any attachment orperipheral device connected to a corresponding body part, such as ahand. For example, hand gestures can be detected by a component of acomputing device to which input via a hand gesture is directed. In turn,detected hand gestures can be forwarded as input commands or informationto the computing device, a software application executing on thecomputing device, and the like. Other embodiments wirelessly detectmacro-gestures or movements, such as a hand wave, a head turn, and soforth. At times, the gesture sensor device transmits each respective RFsignal of the plurality of RF signals on a respective antenna of themultiple antennas. In some embodiments, how the RF signals areconfigured can enhance the quality of information that can be extractedrelative to information extracted when using a random RF signal. Inturn, the enhanced information can be used to identify micro-gestures ofa hand or portions of the hand. For instance, some embodiments provideenough detection resolution to identify micro-gestures performed byfinger(s) of a hand while the body of the hand is stationary. Sometimes,the configuration of the RF signals is based upon information extractiontechniques, such as those that use radar signals. Upon transmitting theRF signals, the gesture sensor device captures, using the multipleantennas, RF signals generated by the transmitted RF signals reflectingoff of the hand. In some cases, the gesture sensor device employs apipeline that receives raw input data representing the captured RFsignals, and extracts information about the hand at different levels ofresolution with respective stages contained within the pipeline. Thepipeline can employ various types of algorithms, such as digital signalprocessing algorithms and machine-learning algorithms. Some embodimentsprovide an ability to modify how the gesture sensor device configuresthe transmitted RF signals, what type of information the gesture sensorextracts, what algorithms are employed to extract the information, howthe information is interpreted, and so forth.

In the following discussion, an example environment is first describedin which various embodiments can be employed. Following this is adiscussion of example RF signal propagation properties and how they canbe employed in accordance with one or more embodiments. After this,wireless micro-gesture techniques are described. Finally, an exampledevice is described in which various embodiments of wireless handgesture detection can be employed.

Example Environment

FIG. 1 illustrates an example environment 100 in which wireless handgesture detection can be employed. The example environment 100 includesa computing device 102 having a gesture sensor component 106 capable ofwirelessly sensing gestures performed by hand 104. In this exampleenvironment, computing device 102 is illustrated as a mobile device, butit is to be appreciated that this is merely for discussion purposes, andthat other devices can be utilized without departing from the scope ofthe claimed subject matter.

Gesture sensor component 106 represents functionality that wirelesslycaptures characteristics of a target object, illustrated here as hand104. In this example, gesture sensor component 106 is a hardwarecomponent 106 of computing device 102. In some cases, gesture sensorcomponent 106 not only captures characteristics about hand 104, but canadditionally identify a specific gesture performed by hand 104 fromother gestures. Any suitable type of characteristic or gesture can becaptured or identified, such as an associated size of the hand, adirectional movement of the hand, a micro-gesture performed by all or aportion of the hand (i.e., a single-tap gesture, a double-tap gesture, aleft-swipe, a forward-swipe, a right-swipe, a finger making a shape,etc.), and so forth. The term micro-gesture is used to signify a gesturethat can be identified from other gestures based on differences inmovement using a scale on the order of millimeters to sub-millimeters.Alternately or additionally, gesture sensor component 106 can beconfigured to identify gestures on a larger scale than a micro-gesture(i.e., a macro-gesture that is identified by differences with a coarserresolution than a micro-gesture, such as differences measured incentimeters or meters).

Hand 104 represents a target object that gesture sensor component 106 isin process of detecting. Here, hand 104 resides in free-space with nodevices attached to it. Being in free-space, hand 104 has no physicaldevices attached to it that couple to, or communicate with, computingdevice 102 and/or gesture sensor component 106. While this example isdescribed in the context of detecting hand 104, it is to be appreciatedthat gesture sensor component 106 can be used to capture characteristicsof any other suitable type of target object.

Signals 108 generally represent multiple RF signals transmitted andreceived by gesture sensor component 106. In some embodiments, gesturesensor component 106 transmits radar signals, each on a respectiveantenna, that are directed towards hand 104. As the transmitted signalsreach hand 104, at least some reflect back to gesture sensor component106 and are processed, as further described below. Signals 108 can haveany suitable combination of energy level, carrier frequency, burstperiodicity, pulse width, modulation type, waveform, phase relationship,and so forth. In some cases, some or all of the respective signalstransmitted in signals 108 differs from one another to create a specificdiversity scheme, such as a time diversity scheme that transmitsmultiple versions of a same signal at different points in time, afrequency diversity scheme that transmits signals using severaldifferent frequency channels, a space diversity scheme that transmitssignals over different propagation paths, and so forth.

Having generally described an environment in which wireless hand gesturedetection may be implemented, now consider FIG. 2 , which illustrates anexample implementation of computing device 102 of FIG. 1 in greaterdetail. As discussed above, computing device 102 represents any suitabletype of computing device in which various embodiments can beimplemented. In this example, various devices include, by way of exampleand not limitation: smartphone 102-1, laptop 102-2, television 102-3,desktop 102-4, tablet 102-5, and camera 102-6. It is to be appreciatedthat these are merely examples for illustrative purposes, and that anyother suitable type of computing device can be utilized withoutdeparting from the scope of the claimed subject matter, such as a gamingconsole, a lighting system, an audio system, etc.

Computing device 102 includes processor(s) 202 and computer-readablemedia 204. Application(s) 206 and/or an operating system (not shown)embodied as computer-readable instructions on the computer-readablemedia 204 can be executed by the processor(s) 202 to provide some or allof the functionalities described herein.

Computer-readable media 204 also includes gesture sensor ApplicationProgramming Interfaces (APIs) 208 to provide programming access intovarious routines and tools provided by gesture sensor component 106. Insome embodiments, gesture sensor APIs 208 provide high-level access intogesture sensor component 106 in order to abstract implementation detailsand/or hardware access from a calling program, request notificationsrelated to identified events, query for results, and so forth. Gesturesensor APIs 208 can also provide low-level access to gesture sensorcomponent 106, where a calling program can control direct or partialhardware configuration of gesture sensor component 106. In some cases,gesture sensor APIs 208 provide programmatic access to inputconfiguration parameters that configure transmit signals (i.e., signals108 of FIG. 1 ) and/or select gesture recognition algorithms. These APIsenable programs, such as application(s) 206, to incorporate thefunctionality provided by gesture sensor component 106 into executablecode. For instance, application(s) 206 can call or invoke gesture sensorAPIs 208 to register for, or request, an event notification when aparticular micro-gesture has been detected, enable or disable wirelessgesture recognition in computing device 102, and so forth. At times,gesture sensor APIs 208 can access and/or include low level hardwaredrivers that interface with hardware implementations of gesture sensorcomponent 106. Alternately or additionally, gesture sensor APIs 208 canbe used to access various algorithms that reside on gesture sensorcomponent 106 to perform additional functionality or extract additionalinformation, such as 3D tracking information, angular extent,reflectivity profiles from different aspects, correlations betweentransforms/features from different channels, and so forth.

Gesture sensor component 106 represents functionality that wirelesslydetects micro-gestures performed by a hand. Gesture sensor component 106can be implemented as a chip embedded within computing device 102.However, it is to be appreciated that gesture sensor component can beimplemented in any other suitable manner, such as one or more IntegratedCircuits (ICs), as a System-on-Chip (SoC), as a processor with embeddedprocessor instructions or configured to access processor instructionsstored in memory, as hardware with embedded firmware, a printed circuitboard with various hardware components, or any combination thereof.Here, gesture sensor component 106 includes antennas 210, digital signalprocessing component 212, machine-learning component 214, and outputlogic component 216. In some embodiments, gesture sensor component 106uses these various components in concert (such as a pipeline) towirelessly detect hand gestures using radar techniques based on multiplesignals.

Antennas 210 transmit and receive RF signals. As one skilled in the artwill appreciate, this is achieved by converting electrical signals intoelectromagnetic waves for transmission, and vice versa for reception.Gesture sensor component 106 can include any suitable number of antennasin any suitable configuration. For instance, any of the antennas can beconfigured as a dipole antenna, a parabolic antenna, a helical antenna,a monopole antenna, and so forth. In some embodiments, antennas 210 areconstructed on-chip (e.g., as part of an SoC), while in otherembodiments, antennas 210 are components, metal, hardware, etc. thatattach to gesture sensor component 106. The placement, size, and/orshape of antennas 210 can be chosen to enhance a specific transmissionpattern or diversity scheme, such as a pattern or scheme designed tocapture information about a micro-gesture performed by the hand, asfurther described above and below. In some cases, the antennas can bephysically separated from one another by a distance that allows gesturesensor component 106 to collectively transmit and receive signalsdirected to a target object over different channels, different radiofrequencies, and different distances. In some cases, antennas 210 arespatially distributed to support triangulation techniques, while inothers the antennas are collocated to support beamforming techniques.While not illustrated, each antenna can correspond to a respectivetransceiver path that physically routes and manages the outgoing signalsfor transmission and the incoming signals for capture and analysis.

Digital signal processing component 212 generally representsfunctionality that digitally captures and processes a signal. Forinstance, digital signal processing component 212 performs sampling onRF signals received by antennas 210 to generate digital samples thatrepresent the RF signals, and processes the digital samples to extractinformation about the target object. Alternately or additionally,digital signal processing component 212 controls the configuration ofsignals transmitted via antennas 210, such as configuring a plurality ofsignals to form a specific diversity scheme, such as a beamformingdiversity scheme. In some cases, digital signal processing component 212receives input configuration parameters that control an RF signal'stransmission parameters (e.g., frequency channel, power level, etc.),such as through gesture sensor APIs 208. In turn, digital signalprocessing component 212 modifies the RF signal based upon the inputconfiguration parameter. At times, the signal processing functions ofdigital signal processing component 212 are included in a library ofsignal processing functions or algorithms that are also accessibleand/or configurable via gesture sensor APIs 208. Digital signalprocessing component 212 can be implemented in hardware, software,firmware, or any combination thereof.

Among other things, machine-learning component 214 receives informationprocessed or extracted by digital signal processing component 212, anduses that information to classify or recognize various aspects of thetarget object, as further described below. In some cases,machine-learning component 214 applies one or more algorithms toprobabilistically determine which gesture has occurred given an inputsignal and previously learned gesture features. As in the case ofdigital-signal processing component 212, machine-learning component 214can include a library of multiple machine-learning algorithms, such as aRandom Forrest algorithm, deep learning algorithms (i.e. artificialneural network algorithms, convolutional neural net algorithms, etc.),clustering algorithms, Bayesian algorithms, and so forth.Machine-learning component 214 can be trained on how to identify variousgestures using input data that consists of example gesture(s) to learn.In turn, machine-learning component 214 uses the input data to learnwhat features can be attributed to a specific gesture. These featuresare then used to identify when the specific gesture occurs. In someembodiments, gesture sensor APIs 208 can be used to configuremachine-learning component 214 and/or its corresponding algorithms.

Output logic component 216 represents functionality that uses logic tofilter output information generated by digital signal processingcomponent 212 and machine-learning component 214. In some cases, outputlogic component 216 uses knowledge about the target object to furtherfilter or identify the output information. For example, consider a casewhere the target object is a hand repeatedly performing a tap gesture.Depending upon its configuration, output logic component 216 can filterthe repeated tap gesture into a single output event indicating arepeated tap gesture, or repeatedly issue a single-tap gesture outputevent for each tap gesture identified. This can be based on knowledge ofthe target object, user input filtering configuration information,default filtering configuration information, and so forth. In someembodiments, the filtering configuration information of output logiccomponent 216 can be modified via gesture sensor APIs 208.

Computing device 102 also includes I/O ports 218 and network interfaces220. I/O ports 218 can include a variety of ports, such as by way ofexample and not limitation, high-definition multimedia (HDMI), digitalvideo interface (DVI), display port, fiber-optic or light-based, audioports (e.g., analog, optical, or digital), Universal Serial Bus (USB)ports, serial advanced technology attachment (SATA) ports, peripheralcomponent interconnect (PCI) express based ports or card slots, serialports, parallel ports, or other legacy ports. Computing device 102 mayalso include the network interface(s) 220 for communicating data overwired, wireless, or optical networks. By way of example and notlimitation, the network interface(s) 220 may communicate data over alocal-area-network (LAN), a wireless local-area-network (WLAN), apersonal-area-network (PAN), a wide-area-network (WAN), an intranet, theInternet, a peer-to-peer network, point-to-point network, a meshnetwork, and the like.

Having described computing device 102 in accordance with one or moreembodiments, now consider a discussion of using wireless detection of anobject in accordance with one or more embodiments.

Propagation of RF Signals

As technology advances, users have an expectation that new devices willprovide additional freedoms and flexibility over past devices. One suchexample is the inclusion of wireless capabilities in a device. Considerthe case of a wireless mouse input device. A wireless mouse input devicereceives input from a user in the format of button clicks and movementin position, and wirelessly transmits this information to acorresponding computing device. The wireless nature obviates the need tohave a wired connection between the wireless mouse input device and thecomputing device, which gives more freedom to the user with the mobilityand placement of the mouse. However, the user still physically interactswith the wireless mouse input device as a way to enter input into thecomputing device. Accordingly, if the wireless mouse input device getslost or is misplaced, the user is unable to enter input with thatmechanism. Thus, removing the need for a peripheral device as an inputmechanism gives additional freedom to the user. One such example isperforming input to a computing device via a hand gesture.

Hand gestures provide a user with a simple and readily availablemechanism to input commands to a computing device. However, detectinghand gestures can pose certain problems. For example, attaching amovement sensing device to a hand does not remove a user's dependencyupon a peripheral device. Instead, it is a solution that simply tradesone input peripheral for another. As an alternative, cameras can captureimages, which can then be compared and analyzed to identify the handgestures. However, this option may not yield a fine enough resolution todetect micro-gestures. An alternate solution involves usage of radarsystems to transmit RF signals to a target object, and determineinformation about that target based upon an analysis of the reflectedsignal.

Various embodiments wirelessly detect hand gestures using multipleantenna. Each antenna can be configured to transmit a respective RFsignal to enable detection of a micro-gesture performed by a hand. Insome embodiments, the collective transmitted RF signals are configuredto radiate a specific transmission pattern or specific diversity scheme.RF signals reflected off of the hand can be captured by the antenna, andfurther analyzed to identify temporal variations in the RF signals. Inturn, these temporal variations can be used to identify micro-gestures.

Consider FIG. 3 which illustrates a simple example of RF wavepropagation, and a corresponding reflected wave propagation. It is to beappreciated that the following discussion has been simplified, and isnot intended to describe all technical aspects of RF wave propagation,reflected wave propagation, or detection techniques.

Environment 300 a includes source device 302 and object 304. Sourcedevice 302 includes antenna 306, which is configured to transmit andreceive electromagnetic waves in the form of an RF signal. In thisexample, source device 302 transmits a series of RF pulses, illustratedhere as RF pulse 308 a, RF pulse 308 b, and RF pulse 308 c. As indicatedby their ordering and distance from source device 302, RF pulse 308 a istransmitted first in time, followed by RF pulse 308 b, and then RF pulse308 c. For discussion purposes, these RF pulses have the same pulsewidth, power level, and transmission periodicity between pulses, but anyother suitable type of signal with alternate configurations can betransmitted without departing from the scope of the claimed subjectmatter.

Generally speaking, electromagnetic waves can be characterized by thefrequency or wavelength of their corresponding oscillations. Being aform of electromagnetic radiation, RF signals adhere to various wave andparticle properties, such as reflection. When an RF signal reaches anobject, it will undergo some form of transition. Specifically, therewill be some reflection off the object. Environment 300 b illustratesthe reflection of RF pulses 308 a-308 c reflecting off of object 304,where RF pulse 310 a corresponds to a reflection originating from RFpulse 308 a reflecting off of object 304, RF pulse 310 b corresponds toa reflection originating from RF pulse 310 b, and so forth. In thissimple case, source device 302 and object 304 are stationary, and RFpulses 308 a-308 c are transmitted via a single antenna (antenna 306)over a same RF channel, and are transmitted directly towards object 304with a perpendicular impact angle. Similarly, RF pulses 310 a-310 c areshown as reflecting directly back to source device 302, rather than withsome angular deviation. However, as one skilled in the art willappreciate, these signals can alternately be transmitted or reflectedwith variations in their transmission and reflection directions basedupon the configuration of source device 302, object 304, transmissionparameters, variations in real-world factors, and so forth. Uponreceiving and capturing RF pulses 310 a-310 c, source device 302 canthen analyze the pulses, either individually or in combination, toidentify characteristics related to object 304. For example, sourcedevice 302 can analyze all of the received RF pulses to obtain temporalinformation and/or spatial information about object 304. Accordingly,source device 302 can use knowledge about a transmission signal'sconfiguration (such as pulse widths, spacing between pulses, pulse powerlevels, phase relationships, and so forth), and further analyze areflected RF pulse to identify various characteristics about object 304,such as size, shape, movement speed, movement direction, surfacesmoothness, material composition, and so forth.

Now consider FIG. 4 , which builds upon the above discussion of FIG. 3 .FIG. 4 illustrates example environment 400 in which multiple antenna areused to ascertain information about a target object. Environment 400includes source device 402 and a target object, shown here as hand 404.Generally speaking, source device 402 includes antennas 406 a-406 d totransmit and receive multiple RF signals. In some embodiments, sourcedevice 402 includes gesture sensor component 106 of FIG. 1 and FIG. 2 ,and antennas 406 a-406 d correspond to antennas 210. While source device402 in this example includes four antennas, it is to be appreciated thatany suitable number of antennas can be used. Each antenna of antennas406 a-406 d is used by source device 402 to transmit a respective RFsignal (e.g., antenna 406 a transmits RF signal 408 a, antenna 406 btransmits RF signal 408 b, and so forth). As discussed above, these RFsignals can be configured to form a specific transmission pattern ordiversity scheme when transmitted together. For example, theconfiguration of RF signals 408 a-408 d, as well as the placement ofantennas 406 a-406 d relative to a target object, can be based uponbeamforming techniques to produce constructive interference ordestructive interference patterns, or alternately configured to supporttriangulation techniques. At times, source device 402 configures RFsignals 408 a-408 d based upon an expected information extractionalgorithm, as further described below.

When RF signals 408 a-408 d reach hand 404, they generate reflected RFsignals 410 a-410 d. Similar to the discussion of FIG. 3 above, sourcedevice 402 captures these reflected RF signals, and then analyzes themto identify various properties or characteristics of hand 404, such as amicro-gesture. For instance, in this example, RF signals 408 a-408 d areillustrated with the bursts of the respective signals being transmittedsynchronously in time. In turn, and based upon the shape and positioningof hand 404, reflected signals 410 a-410 d return to source device 402at different points in time (e.g., reflected signal 410 b is receivedfirst, followed by reflected signal 410 c, then reflected signal 410 a,and then reflected signal 410 d). Reflected signals 410 a-410 d can bereceived by source device 402 in any suitable manner. For example,antennas 406 a-406 d can each receive all of reflected signals 410 a-410d, or receive varying subset combinations of reflected signals 410 a-410d (i.e. antenna 406 a receives reflected signal 410 a and reflectedsignal 410 d, antenna 406 b receives reflected signal 410 a, reflectedsignal 410 b, and reflected signal 410 c, etc.). Thus, each antenna canreceive reflected signals generated by transmissions from anotherantenna. By analyzing the various return times of each reflected signal,source device 402 can determine shape and corresponding distanceinformation associated with hand 404. When reflected pulses are analyzedover time, source device 402 can additionally discern movement. Thus, byanalyzing various properties of the reflected signals, as well as thetransmitted signals, various information about hand 404 can beextracted, as further described below. It is to be appreciated that theabove example has been simplified for discussion purposes, and is notintended to be limiting.

As in the case of FIG. 3 , FIG. 4 illustrates RF signals 408 a-408 d aspropagating at a 90° angle from source device 402 and in phase with oneanother. Similarly, reflected signals 410 a-410 d each propagate back ata 90° angle from hand 404 and, as in the case of RF signals 408 a-408 d,are in phase with one another. However, as one skilled in the art willappreciate, more complex transmission signal configurations, and signalanalysis on the reflected signals, can be utilized, examples of whichare provided above and below. In some embodiments, RF signals 408 a-408d can each be configured with different directional transmission angles,signal phases, power levels, modulation schemes, RF transmissionchannels, and so forth. These differences result in variations betweenreflected signals 410 a-410 d. In turn, these variations each providedifferent perspectives of the target object which can be combined usingdata fusion techniques to yield a better estimate of hand 404, how it ismoving, its 3-dimensional (3D) spatial profile, a correspondingmicro-gesture, etc.

Having described general principles of RF signals which can be used inmicro-gesture detection, now consider a discussion of various forms ofinformation extraction that can be employed in accordance with one ormore embodiments.

Wireless Detection of Micro-Gestures

The above discussion describes simple examples of RF signal transmissionand reflection. In the case of using multiple antenna, it can be seenhow transmitting a plurality of RF signals that have variations from oneanother results in receiving diverse information about a target objectfrom the corresponding reflected signals. The diverse information canthen be combined to improve detecting a characteristic or gestureassociated with the target object. Accordingly, the system as a wholecan exploit or optimize which signals are transmitted to improve theamount of information that can be extracted from the reflected signals.Some embodiments of a gesture sensor component capture raw datarepresentative of signals reflected off a target object. In turn,digital-signal processing algorithms extract information from the rawdata, which can then be fed to a machine-learning algorithm to classifya corresponding behavior of the target object. At times, the gesturesensor component utilizes a pipeline to identify or classify amicro-gesture.

FIG. 5 illustrates the various stages employed by an example pipeline500 to identify micro-gestures using multiple antenna. In someembodiments, pipeline 500 can be implemented by various components ofgesture sensor component 106 of FIGS. 1 and 2 , such as antennas 210,digital signal processing component 212, machine-learning component 214,and/or output logic component 216. It is to be appreciated that thesestages have been simplified for discussion purposes, and are notintended to be limiting. From one viewpoint, the stages can be groupedinto two classifications: transmit side functionality 502 and receiveside functionality 504. Generally speaking, the transmit sidefunctionality in the pipeline does not feed directly into the receiveside functionality. Instead, the transmit side functionality generatestransmit signals which contribute to the reflected signals captured andprocessed by the receive side functionality, as further described above.Accordingly, the relationship between the transmit side functionalityand the receive side functionality is indicated in pipeline 500 throughthe use of a dotted line to connect stage 506 of the pipeline with stage508, rather than a solid line, since in various embodiments they are notdirectly connected with one another.

Stage 506 of the pipeline configures the RF transmit signals. In somecases, various transmission parameters are determined in order togenerate the RF transmit signals. At times, the transmission parameterscan be based upon an environment in which they are being used. Forinstance, the transmission parameters can be dependent upon a number ofantenna available, the types of antenna available, a target object beingdetected, directional transmission information, a requested detectionresolution, a long range object detection mode, a short range objectdetection mode, an expected receive-side digital signal processingalgorithm, an expected receive-side machine-learning algorithm, physicalantenna placement, and so forth. As noted above, the configuration ofthe RF transmit signals can be dependent upon an expected analysis onthe receive side. Thus, the configuration of the RF transmit signals canchange to support triangulation location detection methods, beamformingdetection methods, and so forth. In some embodiments, the transmissionparameters are automatically selected or loaded at startup (e.g., the RFtransmit signal configurations are fixed). In other embodiments, theseparameters are modifiable, such as through gesture sensor APIs 208 ofFIG. 2 .

At the start of receive side functionality 504, stage 508 performssignal pre-processing on raw data. For example, as an antenna receivesreflected signal(s) (such as antennas 406 a-406 d receiving some or allof reflected signals 410 a-410 d of FIG. 4 ), some embodiments samplethe signal(s) and generate a digital representation of the raw(incoming) signals. Upon generating the raw data, stage 508 performspre-processing to clean up the signals or generate versions of thesignals in a desired frequency band, or in a desired format. In somecases, pre-processing includes filtering the raw data to reduce a noisefloor or remove aliasing, resampling the data to obtain to a differentsample rate, generating a complex representation of the signal(s), andso forth. In some cases, stage 508 automatically pre-processes the rawdata based upon default parameters, while in other cases the type ofpre-processing is modifiable, such as through gesture sensor APIs 208 ofFIG. 2 .

Stage 510 transforms the received signal data into one or more differentrepresentations. Here, the signal(s) pre-processed by stage 508 are fedinto stage 510. At times, stage 510 combines data from multiple paths(and corresponding antenna). The combined data can be any combination of“transmit paths”, “receive paths”, and “transmit and receive paths”. Anysuitable type of data fusion technique can be used, such as weightedintegration to optimize an heuristic (i.e., signal-to-noise (SNR) ratio,minimum mean square error (MMSE), etc.), beamforming, triangulation,etc. All respective paths can combined together, or varioussub-combinations of paths can be made, to generate combined signal data.In some embodiments, stage 510 generates multiple combinations of signaldata for different types of feature extraction, and/or transforms thesignal data into another representation as a precursor to featureextraction. For example, some embodiments process the combined signaldata to generate a 3 dimensional (3D) spatial profile of the targetobject. However, any suitable type of algorithm can be used to generatea transformed view or version of the raw data, such as an I/Qtransformation that yields a complex vector containing phase andamplitude information related to the target object, a beamformingtransformation that yields a spatial representation of target objectswithin range of a gesture sensor device, a Range-Doppler algorithm thatyields target velocity and direction, a Range profile algorithm thatyields target recognition information, a Micro-Doppler algorithm thatyields high-resolution target recognition information, a Spectogramalgorithm that yields a visual representation of the correspondingfrequencies, and so forth. As described above, raw data can be processedin several ways to generate several transformations or combined signaldata. At times, the same data can be analyzed or transformed in multipleways. For instance, a same capture of raw data can be processed togenerate a 3D profile, target velocity information, and targetdirectional movement information. In addition to generatingtransformations of the raw data, stage 510 can perform basicclassification of the target object, such as identifying informationabout its presence, a shape, a size, an orientation, a velocity overtime, and so forth. For example, some embodiments use stage 510 toidentify a basic orientation of a hand by measuring an amount ofreflected energy off of the hand over time. These transformations andbasic classifications can be performed in hardware, software, firmware,or any suitable combination. At times, the transformations and basicclassifications are performed by digital signal processing component 212and/or machine-learning component 214 of FIG. 2 . In some cases, stage510 automatically transforms the raw data or performs a basicclassification based upon default parameters, while in other cases thetransformations or classifications are modifiable, such as throughgesture sensor APIs 208 of FIG. 2 .

Stage 512 receives the transformed representation of the data from stage510, and extracts or identifies feature(s) using the data. At times,feature extraction builds upon a basic classification identified instage 510. Consider the above example in which stage 510 classifies atarget object as a hand. Stage 512 can build from this basicclassification to extract lower resolution features of the hand. Inother words, if stage 512 is provided information identifying the targetobject as a hand, then stage 512 uses this knowledge to look forhand-related features (i.e., finger tapping, shape gestures, swipemovements, etc.) instead of head-related features, (i.e., an eye blink,mouthing a word, a head-shaking movement, etc.). As another example,consider a scenario where stage 510 transforms the raw signal data intoa measure of the target object's velocity-over-time. In turn, thisinformation can used by stage 512 to identify a finger fast-tap motionby using a threshold value to compare the target object's velocity ofacceleration to the threshold value, a slow-tap feature, and so forth.Any suitable type of algorithm can be used to extract a feature, such asmachine-learning algorithms implemented by machine-learning component214, and/or digital signal processing algorithms implemented by digitalsignal processing component 212 of FIG. 2 . Some embodiments simplyapply a single algorithm to extract, identify, or classify a feature,while other embodiments apply multiple algorithms to extract a singlefeature or multiple features. Thus, different algorithms can be appliedto extract different types of features on a same set of data, ordifferent sets of data. In some cases, stage 512 searches for a defaultfeature using default algorithm(s), while in other cases the appliedalgorithms and/or the feature being searched for is modifiable, such asthrough gesture sensor APIs 208 of FIG. 2 .

Using feature extraction information generated by stage 512, stage 514performs gesture recognition. For instance, consider a case where afinger tap feature has been extracted. Stage 514 uses this informationto identify the feature as a double-click micro-gesture. At times,gesture recognition can be a probabilistic determination of whichgesture has most likely occurred based upon the input information andhow this information relates to one or more previously learnedcharacteristics or features of various gestures. For example, amachine-learning algorithm can be used to determine how to weightvarious received characteristics to determine a likelihood thesecharacteristics correspond to particular gestures (or components of thegestures). As in the case above, some embodiments apply a singlealgorithm to recognize a gesture, while other embodiments apply multiplealgorithms to identify a single gesture or multiple gestures. This caninclude micro-gestures or macro-gestures. Further, any suitable type ofalgorithm can be used to identify a gesture, such as machine-learningalgorithms implemented by machine-learning component 214, and/or digitalsignal processing algorithms implemented by digital signal processingcomponent 212 of FIG. 2 . In some cases, stage 514 uses defaultalgorithm(s) to identify a gesture, while in other cases the appliedalgorithms and/or the gesture being identified is modifiable, such asthrough gesture sensor APIs 208 of FIG. 2 .

Stage 516 filters output information generated by stage 514 and tracksthis information over time. Referring back to the above example ofidentifying a finger tap micro-gesture, consider now the finger tapmicro-gesture being performed (and identified) repeatedly. Dependingupon the configuration of stage 514, stage 516 might receive multiplenotifications when the micro-gesture is repeatedly identified. However,some recipients looking for a notification of a finger-tap micro-gesturemight desire to be notified once when the finger tapping starts, andthen a second time when the finger tapping stops. Thus, stage 516 canreceive multiple input notifications, where each input notificationcorresponds to a respective instance of a (same) micro-gesture has beenidentified, and then filter the multiple input notifications into oneoutput notification. Alternately or additionally, stage 516 can forwardeach input notification as respective output notifications. At times,stage 516 filters input information from stage 514 according to a set ofparameters. In some embodiments, stage 516 filters the identificationsor notifications using default parameters, while in other embodimentsthe filtering parameters are modifiable, such as through gesture sensorAPIs 208 of FIG. 2 .

Pipeline 500 provides an ability to detect micro-gestures (ormacro-gestures) through the use of multiple antenna. This can includemovements based on portions of a target object, rather than the wholetarget object. Consider again the example case of a target object thatis a hand. On a whole, the hand, or portions of the hand, can be in astationary position while other portions of the hand, such as one ormore fingers, are moving. The above described techniques can be used tonot only identify a stationary hand, but portions of the hand that aremoving, such as two fingers rubbing together. Thus, a micro-gesture canentail identifying a first portion of the hand as being stationary, andidentifying a second portion of the hand as having movement relative tothe stationary portion. Using multiple antenna allows for thetransmission of multiple RF signals, each with different configurations.In turn, when these signals reflect off a target object, differentinformation can be extracted from each respective reflection, thusproviding multiple “vantage point views” of a same target. When theanalyzed together on a whole, these different views of the same targetprovide enough resolution to identify micro-gestures, as well as othertypes of gestures. For instance, a one antenna system cannot recognize atarget object moving towards or away from the antenna due to the angulardistance of the target object changing. However, in the same one antennasystem, it becomes difficult to detect a horizontal, left to rightmovement across the sensor. Conversely, a multiple antenna systemprovides enough diversity in the various signals to make both types ofdirectional detections. Accordingly, when pipeline 500 receivesreflections of these various signals, the various stages extractdifferent levels of resolution in the information. The first stagestarts with a raw captured signal, then generates a transformed versionof the raw signal. The transformed version can be used to extract abasic classification, which can then be used to extracthigher-resolution feature identifications or classifications. A finalstage can then be used to filtered notification outputs of the variousfeature identifications. Thus, pipeline 500 extracts information fromeach respective incoming signal, and combines the extracted informationfrom each respective incoming signal to improve the micro-gestureidentification process.

FIG. 6 is a flow diagram that describes steps in a method in accordancewith one or more embodiments. The method can be implemented inconnection with any suitable hardware, software, firmware, orcombination thereof. In at least some embodiments, the method can beimplemented by a suitably-configured system, such gesture sensorcomponent 106 of FIGS. 1 and 2 .

Step 602 determines transmission parameters for a plurality of outgoingRF signals. In some embodiments, transmission parameters for eachrespective outgoing RF signal are determined independently from oneanother, while in other embodiments, the transmission parameters foreach respective outgoing RF signal are determined in dependence of oneanother to form a specific transmission pattern or specific diversityscheme, as further described above. The determination can be based uponan expected target object type, an expected operating environment,available hardware, anticipated feature or micro-gesture, expectedfeature-extraction algorithm, and so forth. In some cases, one or moregesture sensor APIs can be used to configure some or all of thetransmission parameters for each outgoing RF signals.

Responsive to determining the transmission parameters, step 604transmits the plurality of outgoing RF signals using the determinedtransmission parameters, such as by transmitting each respectiveoutgoing RF signal on a respective antenna. In some embodiments, thedirection of the transmissions can be based upon an expected location ofa target object

Step 606 captures incoming RF signals, such as RF signals reflected ofthe target object, using at least one antenna. As further describedabove, reflected RF signals originate from at least some of theplurality of transmitted RF contacting or connecting with a targetobject. While this example describes reflected signals, it is to beappreciated that other forms of signals can be captured, such asdiffracted waves, refracted waves, scattered waves, and so forth. Attimes, all of the reflected RF signals are captured, while other timessimply a portion of the RF signals are captured. Capturing an RF signalentails receiving at least a portion of the incoming RF signals using atleast one antenna, but multiple antenna can be used. In someembodiments, capturing the reflected RF signals also includes digitizingthe signals through a sampling process.

Responsive to capturing the incoming RF signals, step 608 processes thecaptured incoming RF signals to identify at least one micro-gesture orfeature. For example, the processing can involve multiple stages of apipeline, such as pipeline 500 of FIG. 5 . The processing can be basedupon a type of expected micro-gesture or feature (i.e., the type drivesthe processing algorithms used). In some embodiments, the processingextracts information at multiple levels of resolution, where each stageof processing refines or extracts information at a finer level ofresolution than a previous stage. For instance, a first stage of theprocessing can extract a first set of information about the targetobject at a first level of resolution, such as classifying the targetobject as a hand. This first set of information can then be used asinput to a second stage of processing. In turn, the second stageextracts a second level of information about the target object from thefirst set of information at a finer level of resolution that the firstlevel of information, such as extracting a micro-gesture from multiplehand-related micro-gestures, and so forth. Alternately or additionally,the processing can include filtering an output based upon parameters,such as filtering how often notifications are sent when a micro-gesturehas been detected.

Having considered various embodiments, consider now an example systemand device that can be utilized to implement the embodiments describedabove.

Example Electronic Device

FIG. 7 illustrates various components of an example electronic device700 that incorporates micro-gesture recognition using wirelesstechniques as describe with reference to FIGS. 1-6 . Electronic device700 may be implemented as any type of a fixed or mobile device, in anyform of a consumer, computer, portable, user, communication, phone,navigation, gaming, audio, camera, messaging, media playback, and/orother type of electronic device, such as computing device 102 describedwith reference to FIGS. 1 and 2 . In light of this, it is to beappreciated that various alternate embodiments can include additionalcomponents that are not described, or exclude components that aredescribed, with respect to electronic device 700.

Electronic device 700 includes communication devices 702 that enablewired and/or wireless communication of device data 704 (e.g., receiveddata, data that is being received, data scheduled for broadcast, datapackets of the data, etc.). The device data 704 or other device contentcan include configuration settings of the device and/or informationassociated with a user of the device.

Electronic device 700 also includes communication interfaces 706 thatcan be implemented as any one or more of a serial and/or parallelinterface, a wireless interface, any type of network interface, a modem,and as any other type of communication interface. The communicationinterfaces 706 provide a connection and/or communication links betweenelectronic device 700 and a communication network by which otherelectronic, computing, and communication devices communicate data withelectronic device 700.

Electronic device 700 includes one or more processors 708 (e.g., any ofmicroprocessors, controllers, and the like) which process variouscomputer-executable instructions to control the operation of electronicdevice 700 and to implement embodiments of the techniques describedherein. Alternatively or in addition, electronic device 700 can beimplemented with any one or combination of hardware, firmware, or fixedlogic circuitry that is implemented in connection with processing andcontrol circuits which are generally identified at 710. Although notshown, electronic device 700 can include a system bus or data transfersystem that couples the various components within the device. A systembus can include any one or combination of different bus structures, suchas a memory bus or memory controller, a peripheral bus, a universalserial bus, and/or a processor or local bus that utilizes any of avariety of bus architectures.

Electronic device 700 also includes computer-readable media 712, such asone or more memory components, examples of which include random accessmemory (RAM), non-volatile memory (e.g., any one or more of a read-onlymemory (ROM), flash memory, EPROM, EEPROM, etc.), and a disk storagedevice. A disk storage device may be implemented as any type of magneticor optical storage device, such as a hard disk drive, a recordableand/or rewritable compact disc (CD), any type of a digital versatiledisc (DVD), and the like.

Computer-readable media 712 provides data storage mechanisms to storethe device data 704, as well as various applications 714 and any othertypes of information and/or data related to operational aspects ofelectronic device 700. The applications 714 can include a device manager(e.g., a control application, software application, signal processingand control module, code that is native to a particular device, ahardware abstraction layer for a particular device, etc.).

Electronic device 700 also includes audio and/or video processing system716 that processes audio data and/or passes through the audio and videodata to audio system 718 and/or to display system 720 (e.g., a screen ofa smart phone or camera). Audio system 718 and/or display system 720 mayinclude any devices that process, display, and/or otherwise renderaudio, video, display, and/or image data. Display data and audio signalscan be communicated to an audio component and/or to a display componentvia an RF link, S-video link, HDMI, composite video link, componentvideo link, DVI, analog audio connection, or other similar communicationlink, such as media data port 722. In some implementations, audio system718 and/or display system 720 are external components to electronicdevice 700. Alternatively or additionally, display system 720 can be anintegrated component of the example electronic device, such as part ofan integrated touch interface.

Electronic device 700 also includes gesture sensor component 724 thatwirelessly identifies one or more features of a target object, such as amicro-gesture performed by a hand as further described above. Gesturesensor component 724 can be implemented as any suitable combination ofhardware, software, firmware, and so forth. In some embodiments, gesturesensor component 724 is implemented as an SoC. Among other things,gesture sensor component 724 includes antennas 726, digital signalprocessing component 728, machine-learning component 730, and outputlogic component 732.

Antennas 726 transmit and receive RF signals under the control ofgesture sensor component. Each respective antenna of antennas 726 cancorrespond to a respective transceiver path internal to gesture sensorcomponent 724 that physical routes and manages outgoing signals fortransmission and the incoming signals for capture and analysis asfurther described above.

Digital signal processing component 728 digitally processes RF signalsreceived via antennas 726 to extract information about the targetobject. This can be high-level information that simply identifies atarget object, or lower level information that identifies a particularmicro-gesture performed by a hand. In some embodiments, digital signalprocessing component 728 additionally configures outgoing RF signals fortransmission on antennas 726. Some of the information extracted bydigital signal processing component 728 is used by machine-learningcomponent 730. Digital signal processing component 728 at times includesmultiple digital signal processing algorithms that can be selected ordeselected for an analysis, examples of which are provided above. Thus,digital signal processing component 728 can generate key informationfrom RF signals that can be used to determine what gesture might beoccurring at any given moment.

Machine-learning component 730 receives input data, such as atransformed raw signal or high-level information about a target object,and analyzes the input date to identify or classify various featurescontained within the data. As in the case above, machine-learningcomponent 730 can include multiple machine-learning algorithms that canbe selected or deselected for an analysis. Among other things,machine-learning component 730 can use the key information generated bydigital signal processing component 728 to detect relationships and/orcorrelations between the generated key information and previouslylearned gestures to probabilistically decide which gesture is beingperformed.

Output logic component 732 logically filters output informationgenerated by digital signal processing component 728 and/ormachine-learning component 730. Among other things, output logiccomponent 732 identifies when received information is redundant, andlogically filters the redundancy out to an intended recipient.

Electronic device 700 also includes gesture sensor APIs 734, which areillustrated as being embodied on computer-readable media 712. Gesturesensor APIs 734 provide programmatic access to gesture sensor component724, examples of which are provided above. The programmatic access canrange from high-level program access that obscures underlying details ofhow a function is implemented, to low-level programmatic access thatenables access to hardware. In some cases, gesture sensor APIs can beused to send input configuration parameters associated with modifyingoperation of digital signal processing component 728, machine-learningcomponent 730, output logic component 732, or any combination thereof,examples of which are provided above.

CONCLUSION

Various embodiments wirelessly detect micro gestures using multipleantenna of a gesture sensor device. At times, the gesture sensor devicetransmits multiple outgoing radio frequency (RF) signals, each outgoingRF signal transmitted via a respective antenna of the gesture sensordevice. The outgoing RF signals are configured to help captureinformation that can be used to identify micro-gestures performed by ahand. The gesture sensor device captures incoming RF signals generatedby the outgoing RF signals reflecting off of the hand, and then analyzesthe incoming RF signals to identify the micro-gesture.

Although the embodiments have been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the various embodiments defined in the appended claims are notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as example forms ofimplementing the various embodiments.

What is claimed is:
 1. A method comprising: receiving, by a computingdevice, a plurality of incoming radio frequency (RF) signals generatedby at least one outgoing RF signal reflecting off two or more portionsof a user; processing, by the computing device, a set of dataoriginating from the incoming RF signals to extract information aboutmovements of each of the portions of the user; determining, by thecomputing device and based on the information about the movements ofeach of the portions of the user, a relative movement between theportions of the user; and processing, by the computing device, therelative movement between the portions of the user to identify a gestureperformed by at least one of the portions of the user.
 2. The method ofclaim 1, wherein: the set of data comprises raw data; and the processingthe set of data comprises applying at least one beamforming technique onthe raw data.
 3. The method of claim 1, further comprising: generating,by the computing device, raw data corresponding to the incoming RFsignals; and transforming, by the computing device, the raw data into atleast one transformation, wherein: the transformation comprises one ormore of: an I/Q transformation that yields one or more complex vectorscontaining phase and amplitude information, a beamforming transformationthat yields a spatial representation of the portions of the user, arange-Doppler transformation that yields velocity and directioninformation about the portions of the user, a range profiletransformation that yields recognition information about the portions ofthe user, a micro-Doppler transformation that yields high-resolutionrecognition information about the portions of the user, or a spectrogramtransformation that represents corresponding frequencies of the incomingRF signals; and the set of data comprises the transformation.
 4. Themethod of claim 1, wherein the processing the set of data comprisesfirst processing the set of data at a coarser resolution andsubsequently processing the set of data at a finer resolution.
 5. Themethod of claim 1, wherein the set of data includes Doppler informationabout each of the portions of the user over time.
 6. The method of claim1, wherein the set of data further includes information aboutmillimeter-scale movement of the portions of the user.
 7. The method ofclaim 6, wherein the set of data further includes information aboutmillimeter-scale movement of the portions of the user.
 8. The method ofclaim 1, wherein the portions of the user comprise respective fingers ofa hand of the user.
 9. The method of claim 8, wherein the gesturecomprises the hand of the user forming a certain shape.
 10. The methodof claim 9, wherein the gesture further comprises one or more fingers ina certain orientation relative to other parts of the hand of the user.11. A device configured to identify a relative-movement gestureperformed by a target object, the device comprising: a processor; and acomputer-readable media comprising instructions that, when executed bythe processor, cause the device to: receive a plurality of incomingradio frequency (RF) signals generated by at least one outgoing RFsignal reflecting off the target object; generate signal datacorresponding to the incoming RF signals; determine, based on the signaldata, that the target object has performed the relative-movementgesture, the relative-movement gesture comprising a portion of thetarget object moving relative to another portion of the target object;and output an indication of the relative-movement gesture.
 12. Thedevice of claim 11, wherein the device comprises a smartphone.
 13. Thedevice of claim 11, wherein the instructions further cause the device totransmit the at least one outgoing RF signal using one or more of: timediversity, frequency diversity, or spatial diversity.
 14. The device ofclaim 11, wherein: the instructions further cause the processor toreceive a plurality of other incoming RF signals generated by one ormore other outgoing RF signals reflecting off the target object atrespective previous times; the generation of the signal data comprisesgenerating signal data for the incoming RF signals and other signal datafor the other incoming RF signals; and the determination that the targetobject has performed the relative-movement gesture is based further onthe other signal data.
 15. The device of claim 11, further comprising aplurality of receive antennas, wherein: the receipt of the incoming RFsignals comprises receiving the incoming RF signals via respective onesof the receive antennas; and the generation of the signal data comprisesapplying at least one beamforming technique on the incoming RF signals.16. The device of claim 11, wherein the determination that the targetobject has performed the relative-movement gesture is based further onmillimeter-scale movement of portions of the target object.
 17. Thedevice of claim 16, wherein the determination that the target object hasperformed the relative-movement gesture is based further on determiningthat the millimeter-scale movement of the portions of the target objectcorresponds to a transformation of the target object into a physicalconfiguration or shape.
 18. The device of claim 11, wherein the targetobject is a hand.
 19. The device of claim 18, wherein therelative-movement gesture comprises one or more fingers of the handmoving, or in a certain orientation, relative to other parts of thehand.
 20. The device of claim 18, wherein the relative-movement gesturecomprises a finger of the hand forming a shape.
 21. A device configuredto identify a relative-movement gesture performed by a target object,the device comprising: a processor; and a computer-readable mediacomprising instructions that, when executed by the processor, cause thedevice to: receive a plurality of incoming radio frequency (RF) signalsgenerated by at least one outgoing RF signal reflecting off the targetobject; generate signal data corresponding to the incoming RF signals;determine, based on the signal data and using at least onemachine-learning algorithm, that the target object has performed therelative-movement gesture, the relative-movement gesture comprising aportion of the target object moving relative to another portion of thetarget object; and output an indication of the relative-movementgesture.
 22. The device of claim 21, wherein the machine-learningalgorithm is trained using other signal data corresponding to otherinstances of the relative-movement gesture.
 23. The device of claim 21,wherein the machine-learning algorithm comprises one or more of a RandomForrest algorithm, a deep learning algorithm, an artificial neuralnetwork algorithm, a convolutional neural net algorithm, a clusteringalgorithm, or a Bayesian algorithm.
 24. The device of claim 21, wherein:the signal data comprises raw data; the instructions further cause thedevice to transform, using at least one other machine-learningalgorithm, the signal data into a different representation; and thedetermination that the target object has performed the relative-movementgesture is based further on the different representation.
 25. The deviceof claim 21, wherein: the target object is a hand; and therelative-movement gesture further comprises one or more fingers of thehand moving relative to other parts of the hand.
 26. The device of claim21, wherein: the signal data comprises raw data; the instructionsfurther cause the device to transform the signal data into a differentrepresentation; the different representation comprises one or more of:an I/Q transformation that yields one or more complex vectors containingphase and amplitude information, a beamforming transformation thatyields a spatial representation of the target object, a range-Dopplertransformation that yields velocity and direction information about thetarget object, a range profile transformation that yields recognitioninformation about the target object, a micro-Doppler transformation thatyields high-resolution recognition information about the target object,or a spectrogram transformation that represents correspondingfrequencies of the incoming RF signals; and the determination that thetarget object has performed the relative-movement gesture comprisesinputting the different representation into the at least onemachine-learning algorithm.
 27. The device of claim 21, wherein therelative-movement gesture is three-dimensional.
 28. The device of claim21, wherein the determination that the target object has performed therelative-movement gesture comprises: determining, using a first stage, aclassification of the target object; and determining, using a secondstage, that the target object has performed the relative-movementgesture.
 29. The device of claim 28, wherein the second stage is basedon the classification of the target object.
 30. The device of claim 28,wherein the classification of the target object comprises aclassification of the target object as a certain body part.
 31. A deviceconfigured to identify a relative-movement gesture performed by a targetobject, the device comprising: at least one transmit antenna; aplurality of receive antennas; a processor; and a computer-readablemedia comprising instructions that, when executed by the processor,cause the device to: receive a plurality of incoming radio frequency(RF) signals generated by at least one outgoing RF signal reflecting offthe target object, each of the incoming RF signals received by one ofthe receive antennas; generate signal data corresponding to the incomingRF signals; determine, based on the signal data, that the target objecthas performed the relative-movement gesture, the relative-movementgesture comprising a first portion of the target object moving relativeto a second portion of the target object; and output an indication ofthe relative-movement gesture.
 32. The device of claim 31, wherein thereceive antennas are spatially distributed to support triangulationbetween the device and the first and second portions of the targetobject.
 33. The device of claim 31, wherein: the instructions furthercause the device to determine a spatial profile of the target object;and the determination that the target object has performed therelative-movement gesture is based further on the spatial profile of thetarget object.
 34. The device of claim 33, wherein the at least onetransmit antenna comprises a plurality of transmit antennas.
 35. Thedevice of claim 34, wherein the instructions further cause the device totransmit the at least one outgoing RF signal using constructive ordestructive interference.
 36. The device of claim 31, wherein: thesignal data comprises raw data; the instructions further cause thedevice to transform the signal data into a different representation; thedifferent representation comprises one or more of: an I/Q transformationthat yields one or more complex vectors containing phase and amplitudeinformation, a beamforming transformation that yields a spatialrepresentation of the target object, a range-Doppler transformation thatyields velocity and direction information about the target object, arange profile transformation that yields recognition information aboutthe target object, a micro-Doppler transformation that yieldshigh-resolution recognition information about the target object, or aspectrogram transformation that represents corresponding frequencies ofthe incoming RF signals; and the determination that the target objecthas performed the relative-movement gesture is based further on thedifferent representation.
 37. The device of claim 36, wherein therelative-movement gesture comprises a swipe or tap gesture.
 38. Thedevice of claim 31, wherein the signal data comprises respective signaldata for the first and second portions of the target object.
 39. Thedevice of claim 38, wherein the signal data comprises movementinformation corresponding to the first and second portions of the targetobject.
 40. The device of claim 39, wherein: the target object is ahand; the first and second portions of the target object are respectivefingers of the hand; and the relative-movement gesture comprises amovement between the fingers of the hand.